Genes and Gene Networks Related to Age-associated Learning Impairments

The incidence of cognitive impairments, including age-associated spatial learning impairment (ASLI), has risen dramatically in past decades due to increasing human longevity. To better understand the genes and gene networks involved in ASLI, data from a number of past gene expression microarray studies in rats are integrated and used to perform a meta- and network analysis. Results from the data selection and preprocessing steps show that for effective downstream analysis to take place both batch effects and outlier samples must be properly removed. The meta-analysis undertaken in this research has identified significant differentially expressed genes across both age and ASLI in rats. Knowledge based gene network analysis shows that these genes affect many key functions and pathways in aged compared to young rats. The resulting changes might manifest as various neurodegenerative diseases/disorders or syndromic memory impairments at old age. Other changes might result in altered synaptic plasticity, thereby leading to normal, non-syndromic learning impairments such as ASLI. Next, I employ the weighted gene co-expression network analysis (WGCNA) on the datasets. I identify several reproducible network modules each highly significant with genes functioning in specific biological functional categories. It identifies a “learning and memory” specific module containing many potential key ASLI hub genes. Functions of these ASLI hub genes link a different set of mechanisms to learning and memory formation, which meta-analysis was unable to detect. This study generates some new hypotheses related to the new candidate genes and networks in ASLI, which could be investigated through future research

SCTIGER: A DEEP-LEARNING METHOD FOR INFERRING GENE REGULATORY NETWORKS FROM SINGLE-CELL GENE EXPRESSION DATA

Inferring gene regulatory networks (GRNs) from single-cell RNA-sequencing (scRNA-seq) data is an important computational question to reveal fundamental regulatory mechanisms. Although many computational methods have been designed to predict GRNs, none work on condition specific GRNs by directly using paired datasets of case versus control experiments, common in diverse biological research projects. We present a novel deep-learning based method, scTIGER, for GRN detection by using the co-dynamics of gene expression. scTIGER also employs cell type-based pseudotiming, an attention-based convolutional neural network method, and permutation-based significance testing to infer GRNs from gene modules. We first applied scTIGER to scRNA-seq datasets of prostate cancer cells and detected potential AR-mediated GRNs. Then, when applied to mouse neurons with and without fear memory and detected CREB-mediated GRNs. The results show scTIGER can be applied to general case-versus-control scRNA-seq datasets with high performance

Using single-cell technologies to map the human immune system - implications for nephrology.

Advances in single-cell technologies are transforming our understanding of cellular identity. For instance, the application of single-cell RNA sequencing and mass cytometry technologies to the study of immune cell populations in blood, secondary lymphoid organs and the renal tract is helping researchers to map the complex immune landscape within the kidney, define cell ontogeny and understand the relationship of kidney-resident immune cells with their circulating counterparts. These studies also provide insights into the interactions of immune cell populations with neighbouring epithelial and endothelial cells in health, and across a range of kidney diseases and cancer. These data have translational potential and will aid the identification of drug targets and enable better prediction of off-target effects. The application of single-cell technologies to clinical renal biopsy samples, or even cells within urine, will improve diagnostic accuracy and assist with personalized prognostication for patients with various kidney diseases. A comparison of immune cell types in peripheral blood and secondary lymphoid organs in healthy individuals and in patients with systemic autoimmune diseases that affect the kidney will also help to unravel the mechanisms that underpin the breakdown in self-tolerance and propagation of autoimmune responses. Together, these immune cell atlases have the potential to transform nephrology

Transcriptomic Profiling in Mild Cognitive Impairment and Alzheimer's Disease Using Neuroimaging Endophenotypes

Indiana University-Purdue University Indianapolis (IUPUI)Alzheimer’s disease (AD) is a devastating neurodegenerative disease affecting more than 6 million Americans and 50 million people worldwide currently. It is an irreversible neurodegenerative disease which causes decline in memory, cognition, personality, and other functions which eventually lead to death due to complete brain failure. Recently there has been a lot of research that has focused on enabling early intervention and disease prevention in AD which could have a significant impact on this disease, be crucial for life management, assessment of risk for future generations, and assistance in end-of-life preparation. For a late-life complex multifactorial disease, such as AD, where both genetic and environmental factors are involved, integrating multiple layers of genetic, imaging, and other biomarker data is a critical step for therapeutic discovery and building predictive risk assessment tools. The multifactorial nature of AD suggests that multiple therapeutic targets need to be identified and tested together. Hence, we need a systems-level approach to build biomarker profiles which can be used for drug discovery and screening/risk assessment. The research presented in this dissertation focuses on utilizing a systems level approach to identify promising imaging genetics biomarkers that provide insight into dysregulated biological pathways in AD pathogenesis and identify critical mRNA measures that can be investigated further within the scope of novel therapeutics, as well as input variables in predictive models for AD risk, screening, and diagnosis. The overall research goal was the development of systems level, imaging genetics biomarker signatures to serve as tools for risk analysis and therapeutic discovery in AD. The specific outcomes of the analyses were characterization of patterns in gene expression at systems level using neuroimaging endophenotypes, and identification of specific driver genes and genotypic variants, which can inform predictive modeling for diagnosis, risk, and pathogenic profiling in AD

Etude expérimentale des dynamiques temporelles du comportement normal et pathologique chez le rat et la souris

155 p.Modern neuroscience highlights the need for designing sophisticated behavioral readout of internal cognitive states. From a thorough analysis of classical behavioral test, my results supports the hypothesis that sensory ypersensitivity might be the cause of other behavioural deficits, and confirm the potassium channel BKCa as a potentially relevant molecular target for the development of drug medication against Fragile X Syndrome/Autism Spectrum Disorders. I have also used an innovative device, based on pressure sensors that can non-invasively detect the slightest animal movement with unprecedented sensitivity and time resolution, during spontaneous behaviour. Analysing this signal with sophisticated computational tools, I could demonstrate the outstanding potential of this methodology for behavioural phenotyping in general, and more specifically for the investigation of pain, fear or locomotion in normal mice and models of neurodevelopmental and neurodegenerative disorders

【研究分野別】シーズ集 [英語版]

[英語版

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u

The Role of Mutations in Protein Structural Dynamics and Function: A Multi-scale Computational Approach

abstract: Proteins are a fundamental unit in biology. Although proteins have been extensively studied, there is still much to investigate. The mechanism by which proteins fold into their native state, how evolution shapes structural dynamics, and the dynamic mechanisms of many diseases are not well understood. In this thesis, protein folding is explored using a multi-scale modeling method including (i) geometric constraint based simulations that efficiently search for native like topologies and (ii) reservoir replica exchange molecular dynamics, which identify the low free energy structures and refines these structures toward the native conformation. A test set of eight proteins and three ancestral steroid receptor proteins are folded to 2.7Å all-atom RMSD from their experimental crystal structures. Protein evolution and disease associated mutations (DAMs) are most commonly studied by in silico multiple sequence alignment methods. Here, however, the structural dynamics are incorporated to give insight into the evolution of three ancestral proteins and the mechanism of several diseases in human ferritin protein. The differences in conformational dynamics of these evolutionary related, functionally diverged ancestral steroid receptor proteins are investigated by obtaining the most collective motion through essential dynamics. Strikingly, this analysis shows that evolutionary diverged proteins of the same family do not share the same dynamic subspace. Rather, those sharing the same function are simultaneously clustered together and distant from those functionally diverged homologs. This dynamics analysis also identifies 77% of mutations (functional and permissive) necessary to evolve new function. In silico methods for prediction of DAMs rely on differences in evolution rate due to purifying selection and therefore the accuracy of DAM prediction decreases at fast and slow evolvable sites. Here, we investigate structural dynamics through computing the contribution of each residue to the biologically relevant fluctuations and from this define a metric: the dynamic stability index (DSI). Using DSI we study the mechanism for three diseases observed in the human ferritin protein. The T30I and R40G DAMs show a loss of dynamic stability at the C-terminus helix and nearby regulatory loop, agreeing with experimental results implicating the same regulatory loop as a cause in cataracts syndrome.Dissertation/ThesisPh.D. Physics 201

Analysis of Subchondral Bone and Microvessels Using a Novel Vascular Perfusion Contrast Agent and Optimized Dual-Energy Computed Tomography

Osteoarthritis (OA), is a chronic debilitating disease that affects millions of individuals and is characterized by the degeneration of joint subchondral bone and cartilage. These tissue degenerations manifest as joint pain, limited range of joint motion, and overall diminished quality of life. Currently, the exact mechanism(s) and cause(s) by which OA initiates and progresses remain unknown. The multi-factorial complex nature of OA (i.e. age, diabetes, obesity, and prior injuries have all been shown to play a role in OA) contributes to the current lack of a cure or effective long-term treatment for OA. One re-emerging and interesting hypothesis revolves around the delicate homeostatic microvascular environment around the cartilage – an avascular tissue. The absence of blood vessels within cartilage stresses the importance of nutrient and oxygen delivery from the neighbouring synovium and subchondral bone. Currently, the effects of changes in the subchondral bone microvessel density on cartilage health remain unknown due to the difficulties in simultaneously studying dense bone and the associated small microvessels. Computed tomography (CT) is widely used in the diagnosis of OA, as the use of x-rays provide detailed images of the bone degeneration associated with OA. However, the study of microvessels using CT has been exceptionally difficult due to their small (\u3c 10 µm) size, lack of contrast from neighbouring soft tissues, and proximity to dense bone. The purpose of this thesis was to develop a novel dual-energy micro-computed tomography (DECT) compatible vascular perfusion contrast agent and the associated instrumentation to optimize DECT on pre-clinical, cone-beam micro-CT scanners. The combination of these two techniques would facilitate the simultaneous visualization and quantification of subchondral bone and microvessels within the bone underlining the cartilage (i.e. distal femoral epiphysis and proximal tibial epiphysis) of rats that have undergone an OA-induced surgery. Results gained from this study will further provide information into the role that microvessels may play in OA

A Review of Recent Gene Expression-Based and DNA Methylation-Based Mathematical Cell Type Deconvolution Methods

In recent years, many cell type deconvolution methods based on DNA methylation data and gene expression data have been developed. Both of these two methods have its special advantages and disadvantages, e.g., DNA methylation-based methods’ data source is usually more stable than gene expression and DNA methylation is easier to measure in FFPE tissues or formalin-fixed paraffin-embedded, while some gene-expression data like scRNA-seq data usually has high cost and complexity. On the other hand, gene expression-based deconvolution methods currently have many more available methods than DNA methylation-based deconvolution methods, which leads to DNA methylation-based methods in many cases can learn from the existing gene expression-based methods, e.g., the EMeth learns from ICeD-T while the MethylCIBERSORT learns from CIBERSORT. Since both of these two kinds of different data-based methods are powerful tools to realize the purpose of cell type-specific deconvolution and may could benefit each other’s development, as well as they have been still rapidly developing in recent years with believably more coming new methods in the future. It may be well worth looking back and comparing some recent gene expression data-based and DNA methylation-based deconvolution methods to get some comprehensive sense of this field’s development and directions on both two different data-based deconvolution method